Valuation Atlas
Associated with the IPBES assessment on the diverse values and valuation of nature and its contributiosn to people
Joy A. Kumagai and Aidin Niamir
23/9/2021
Overview
The aim was to prepare a spatio-temporal representation of valuation studies related to biodiversity and ecosystem services and … .
1 Creation of the Corpus
1.1 Methodology
1.2 Description of the corpus
2 Georeferencing the Corpus
To identify country names in the corpus of literature a two step approach was used. First, we wanted to understand where studies were conducted and searched the title, abstract, and keywords of each paper for country names. Second, to understand where the funding institutions were located we searched the affiliations, acknowledgments, and funding text for country names.
The input data we used are the following:
Bib file downloaded from Web of Science
ISO 3166-1 alpha-3 country codes (https://www.iso.org/iso-3166-country-codes.html)
IPBES regional and subregional area dataset (https://doi.org/10.5281/zenodo.3923633)
The python code used to georeference the corpus can be found here. An overview of the pipeline is provided in the following schematic and described below.
knitr::include_graphics("pilot2.svg")
Overview of the process of Georeferencing the corpus of valuation studies
Step 1: Extract country names from text Country names were extracted from the title, abstract, and keywords of each paper with a regular expression and the associated ISO code was added into a a column in the dataset. The same regular expression was also used to search the affiliations, acknowledgments, and funding text of the same paper and placed into a second column.
Step 2 and 3: Bundle countries in regions and subregions The IPBES Regions and Subregions datatset was then used to add additional region and subregion attributes to the dataset by matching the ISO3 code.
Step 4: Find TS accordingly We used a set of files to add additional attributes to the dataset that identified the topics. The set of files contained identifying information for papers derived from sets of web of science searches targeting particular topics. This identifying information was then matched to the corpus, and the topic extracted.
Finally, the complete corpus with the added attributes of country ISO codes of both funding institutions and research locations, and topic identification were used as the basis of the rest of the research project. The complete corpus can be found on Zenodo here: [https://doi.org/[INSERT](https://doi.org/%5BINSERT){.uri} DOI]
3 Valuation Maps
The complete georeferenced valuation corpus was used to understand the location of valuation studies throughout the world and the location of the institutions conducting those studies.
We counted the number of times a country was listed in the corpus from the column of country names obtained from the title, abstract, and keywords search as a proxy for the density of valuation studies. in Part 4 - Indicator Analysis this is referred to as “Names 1”.
Separately, we counted the number of times a country was listed in the corpus from the column of country names obtained from the affiliations acknowledgments, and funding text as a proxy for the density of institutions. In Part 4 - Indicator Analysis this is referred to as “Names 2”
3.1 Geographic Distribution
We will now present the results from the analysis through a series of maps showing both the raw country results and summarized by IPBES regions and subregions. No data is always displayed in grey.
3.1.1 Density of studies
30,428 studies did not identify a country within their title, abstract, or keywords.
All
Showcases the number of valuation studies conducted within each country for the entire dataset. Please note that the scales are not consistent between maps.
knitr::include_graphics("Outputs/Maps/Names1_percountry.png")
Before 2010
Showcases the number of valuation studies conducted within each country between 1980 - 2009. Please note that the scales are not consistent between maps.
knitr::include_graphics("Outputs/Maps/Names1_percountry_before2010.png")
After 2010
Showcases the number of valuation studies conducted within each country between 2010 - 2020. Please note that the scales are not consistent between maps.
knitr::include_graphics("Outputs/Maps/Names1_percountry_2010.png")
3.1.2 Density of institutions
376 studies did not identify a country within their affiliations, acknowledgments, and funding text.
All
Showcases the number of studies which reference the particular country within the affiliations, acknowledgments, or funding text for the entire dataset as a proxy for institutions which are conducing the research. Please note that the scales are not consistent between maps.
knitr::include_graphics("Outputs/Maps/Names2_percountry.png")
Before 2010
Showcases the number of studies which reference the particular country within the affiliations, acknowledgments, or funding text between 1980 and 2009 as a proxy for institutions which are conducing the research in this time period. Please note that the scales are not consistent between maps.
knitr::include_graphics("Outputs/Maps/Names2_percountry_before2010.png")
After 2010
Showcases the number of studies which reference the particular country within the affiliations, acknowledgments, or funding text between 2010 and 2020 as a proxy for institutions which are conducing the research in this time period. Please note that the scales are not consistent between maps.
knitr::include_graphics("Outputs/Maps/Names2_percountry_2010.png")
3.1.3 Summarized by region and subregion
Density of studies by region
knitr::include_graphics("Outputs/Maps/Names1_perregion.png")
*30,428 studies did not identify a country within their title, abstract, or keywords.
Density of institutions by region
knitr::include_graphics("Outputs/Maps/Names2_perregion.png")
- 376 studies did not identify a country within their affiliations, acknowledgments, and funding text.
Density of studies by subregion
knitr::include_graphics("Outputs/Maps/Names1_persubregion.png")
- 376 studies did not identify a country within their affiliations, acknowledgments, and funding text.
Density of institutions by subregion
knitr::include_graphics("Outputs/Maps/Names2_persubregion.png")
*30,428 studies did not identify a country within their title, abstract, or keywords.
3.2 Relationships between density of studies and institutions
While more instuttions are identified per study compared to where the study was conducted, it is clear that there is a significant positive linear realtionship between these.
knitr::include_graphics("Outputs/Corpus/Names1_Names2.png")
Density of studies vs density of institutions
3.3 Temporal Trends
There has been an exponential increase in valuation studies from the 1980’s to the present as pictured in the graph below.
knitr::include_graphics("Outputs/Corpus/Timeline.svg")
- 569 studies did not have a year identified
- Data from 2020 was removed due to incomplete sampling of 2020.
3.3.1 Geographic density of studies
Additionally, we explored how the density of valuation studies has changed through time from 1980 to 2020. No data is displayed in grey.
knitr::include_graphics("Outputs/Maps/Names1_fourpanels.png")
3.3.2 Geographic density of institutions
Additionally, we explored how the density of valuation institutions has changed through time from 1980 to 2020. No data is displayed in grey.
knitr::include_graphics("Outputs/Maps/Names2_fourpanels.png")
4 Indicator Analysis
The IPBES Core Indicators were used alongside a chosen set of other relevant indicators to understand geographic trends between density of valuation studies and how they relate to biological and socioeconomic indicators.
4.1 Selection of Indicators
We used all the most recent year of the IPBES Core Indicators available within the country dataset except for two indicators, Countries/Regions with Active NBSAP and Category 1 nations in CTIES, as these are binary in the dataset and would not be compatible with the following analysis. We selected a specific category from the indicators with multiple categories. For example, for the indicator “Area of forest production under FSC and PEFC certification” we chose the FSC certification area and not the PEFC certification area.
A set of other indicators were included in the analysis to expand the coverage of socioeconomic variables. We included the human development index (HDI), average harmonized learning outcomes score, gross domestic product (GDP), corruption perception index (CPI), and population.
These datasets were downloaded, cleaned, and had ISO3 codes added to easily merge them into the analysis. The latest data available was used for each indicator.
Here is the table of all of the indicators used, the category selected, the year the data is from, and the number assigned to them.
| Name | Category | Year |
|---|---|---|
| Area of forest production under FSC and PEFC certification | FSC_area | 2016 |
| Biodiversity Habitat Index | Average | 2014 |
| Biodiversity Intactness Index | Value | 2005 |
| Biocapacity per capita | Value - Total | 2012 |
| Ecological Footprint per capita | Value - Total | 2012 |
| Forest area | Forest area (1000ha) | 2015 |
| Water Footprint | Water Footprint - Total (Mm3/y) | 2013 |
| Inland Fishery Production | Capture | 2015 |
| Region-based Marine Trophic Index | 1950 | 2014 |
| Nitrogen + Phosphate Fertilizers | N total nutrients - Consumption in nutrients | 2014 |
| Nitrogen Use Efficiency (%) | Nitrogen Use Efficiency (%) | 2009 |
| Percentage and total area covered by protected areas | Terrestrial - Protected Area (%) | 2017 |
| Percentage of undernourished people | Prevalence of undernourishment (%) (3-year average) | 2015 |
| Proportion of local breeds, classified as being at risk, not-at-risk or unknown level of risk of extinction | At Risk of Extinction | 2016 |
| PA of Key Biodiversity Areas Coverage (%) | Estimate | 2016 |
| Protected area management effectiveness | PA Assessed on Management Effectiveness (%) | 2015 |
| Protected Area Connectedness Index | Protected Area Connectedness Index | 2012 |
| Species Habitat Index | Species Habitat Index | 2014 |
| Species Protection Index (%) | Species Protection Index (%) | 2014 |
| Species Status Information Index | Value | 2014 |
| Total Wood Removals (roundwood, m3) | Total | 2014 |
| Trends in forest extent (tree cover) | Percentage of Tree Cover Loss | 2015 |
| Nitrogen Deposition Trends (kg N/ha/yr) | Nitrogen Deposition Trends (kg N/ha/yr) | 2030 |
| Trends in Pesticides Use | Use of pesticides (3-year average) | 2013 |
| Human Development Index (HDI) | NA | 2018 |
| Average harmonized learning outcomes score | NA | 2015 |
| Gross domestic product (GDP) | NA | 2019 |
| Corruption perception index (CPI) | NA | 2020 |
| Population | NA | 2018 |
There were a few instances of duplicated values which were double checked with the original dataset and the erranous value removed. Examples include having two values for USA due to the separation of Hawaii in the original dataset. In these cases Hawaii was removed and the value referring to the rest of the states of the country was used instead. Additionally, Indicator 9, Region-based Marine Trophic Index, the mean of the regions was calculated per country, as countries such as Germany have multiple regions with distinct values.
4.2 Dataset Compilation
To understand how valuation is spread across geographies, we counted the number of times each country’s ISO code appeared in the corpus for both geography columns added in step 2. The result is the density of studies per country and the density of funding institutions per country for the entire corpus.
The external indicators were also joined onto the dataset to analyze the relationships between these socioeconomic indicators and the density of studies and funding institutions.
This process was also repeated with an additional filter that excluded any studies published before 2010.
4.3 Pearson Correlation Analysis
To investigate the relationships between indicators and the number of valuation studies, we ran a pearson correlation analysis. The statistical analysis calculated the trends between the number of studies in each country (Names 1) and the number of studies funded in each country (Names 2) and each of the indicators. The results are shown below.
Insignificant relationships are blank, significant relationships (P < 0.01) are shown with circles. The strength of the correlation corresponds to the size of the circle and the color represents positve (blue) or negative (red) trends.
knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure.png")
Pearson correlations of geographic valuation studies and indicators
The same pearson correlation analysis with the indicators was conducted on the log of the number of studies in each country (Names 1) and the log of the number of studies funded from each country (Names2).
knitr::include_graphics("Outputs/Pearson_correlation_table/correlation_figure_log.png")
Pearson correlations of log geographic valuation studies and the indicators
4.4 Individual Trend Analysis
The individual trends between each indicator and the number of studies and number of funding institutions are shown here for the entire corpus. For each indicator, there are two four panel figures with the top figure showing the trends with the raw values, and the bottom figure displaying the trends with log transformed x-axis values.
The dots represent countries within the dataset that have values for both the indicator and valuation atlas. The associated p value of the linear trend model is shown in the corner of the image in red. The trend line is shown in blue and the associated standard error in grey. Please note that the y axis is not necessarily consistent between the four boxes and may include values that aren’t actually found in the data to showcase the full extent of the standard error of the trend line.
For each figure the panels are arranged accordingly:
Names 1 (top-left) is the number of valuation studies for each country in the corpus
Names 1 from 2010 (top-right) is the number of valuation studies for each country in the corpus from 2010 to 2020
Names 2 (bottom-left) is the number of valuation studies funded from each country in the corpus
Names 2 from 2010 (bottom-right) is the number of valuation studies funded from each country in the corpus from 2010 to 2020
Human Development Index
knitr::include_graphics("Outputs/Figures_Corpus_individual/HDI.svg")
Relationships between valuation atlas and the human development index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/HDI_log.svg")
Log relationships between valuation atlas and the human development index
Learning Outcomes
knitr::include_graphics("Outputs/Figures_Corpus_individual/LearningOutcomes.svg")
Relationships between valuation atlas and learning outcomes
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/LearningOutcomes_log.svg")
Log relationships between valuation atlas and learning outcomes
GDP
knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP.svg")
Relationships between valuation atlas and GDP
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_log.svg")
Log relationships between valuation atlas and GDP
Corruption Perception Index
knitr::include_graphics("Outputs/Figures_Corpus_individual/CPI.svg")
Relationships between valuation atlas and the corruption perception index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/CPI_log.svg")
Log relationships between valuation atlas and the corruption perception index
Population
knitr::include_graphics("Outputs/Figures_Corpus_individual/POP.svg")
Relationships between valuation atlas and population
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/POP_log.svg")
Log relationships between valuation atlas and population
GDP per capita
knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_per_capita.svg")
Relationships between valuation atlas and GDP per capita
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/GDP_per_capita_log.svg")
Log relationships between valuation atlas and GDP per capita
Forest area under FSC certification
knitr::include_graphics("Outputs/Figures_Corpus_individual/FSC.svg")
Relationships between valuation atlas and forest area under FSC certification
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/FSC_log.svg")
Log relationships between valuation atlas and forest area under FSC certification
Biodiversity Habitat Index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_habitat_index.svg")
Relationships between valuation atlas and the biodiversity habitat index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_habitat_index_log.svg")
Log relationships between valuation atlas and the biodiversity habitat index
Biodiversity Intactness Index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_intactness_index.svg")
Relationships between valuation atlas and the biodiversity intactness index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biodiversity_intactness_index_log.svg")
Log relationships between valuation atlas and the biodiversity intactness index
Biocapacity per capita
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biocapacity_per_capita.svg")
Relationships between valuation atlas and the biocapacity per capita
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Biocapacity_per_capita_log.svg")
Log relationships between valuation atlas and the biocapacity per capita
Ecological Footprint per capita
knitr::include_graphics("Outputs/Figures_Corpus_individual/Ecological_footprint.svg")
Relationships between valuation atlas and the ecological footprint per capita
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Ecological_footprint_log.svg")
Log relationships between valuation atlas and the ecological footprint per capita
Forest area
knitr::include_graphics("Outputs/Figures_Corpus_individual/Forest_area.svg")
Relationships between valuation atlas and forest area
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Forest_area_log.svg")
Log relationships between valuation atlas and forest area
Water Footprint
knitr::include_graphics("Outputs/Figures_Corpus_individual/Water_footprint.svg")
Relationships between valuation atlas and the water footprint
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Water_footprint_log.svg")
Log relationships between valuation atlas and the water footprint
Inland Fishery Production
knitr::include_graphics("Outputs/Figures_Corpus_individual/Inland_fishery_production.svg")
Relationships between valuation atlas and inland fishery production
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Inland_fishery_production_log.svg")
Log relationships between valuation atlas and inland fishery production
Region-based Marine Trophic Index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Marine_tropic_index.svg")
Relationships between valuation atlas and the region-based marine trophic index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Marine_tropic_index_log.svg")
Log relationships between valuation atlas and the region-based marine trophic index
Nitrogen Fertilizers
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_fertilizers.svg")
Relationships between valuation atlas and nitrogen fertization
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_fertilizers_log.svg")
Log relationships between valuation atlas and nitrogen fertization
Nitrogen Use Efficiency (%)
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_use_efficiency.svg")
Relationships between valuation atlas and nitrogen use efficiency
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_use_efficiency_log.svg")
Log relationships between valuation atlas and nitrogen use efficiency
Percentage of area covered by protected areas
knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_protected.svg")
Relationships between valuation atlas and percentage of area covered by protected areas
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_protected_log.svg")
Log relationships between valuation atlas and percentage of area covered by protected areas
Percentage of undernourished people
knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_of_undernourished_people.svg")
Relationships between valuation atlas and percentage of undernourished people
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Percentage_of_undernourished_people_log.svg")
Log relationships between valuation atlas and percentage of undernourished people
Local Breeds at risk of extinction
knitr::include_graphics("Outputs/Figures_Corpus_individual/Local_breeds.svg")
Relationships between valuation atlas and local breeds at risk of extinction
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Local_breeds_log.svg")
Log relationships between valuation atlas and local breeds at risk of extinction
PA of Key Biodiversity Areas Coverage (%)
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_of_key_biodiversity_area_coverage.svg")
Relationships between valuation atlas and PA of key biodiverisy area coverage
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_of_key_biodiversity_area_coverage_log.svg")
Log relationships between valuation atlas and PA of key biodiverisy area coverage
Protected area management effectiveness
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_management_effectiveness.svg")
Relationships between valuation atlas and protected area management effectiveness
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_management_effectiveness_log.svg")
Log relationships between valuation atlas and protected area management effectiveness
Protected Area Connectedness Index
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_connectedness.svg")
Relationships between valuation atlas and the protected area connectedness index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/PA_connectedness_log.svg")
Log relationships between valuation atlas and the protected area connectedness index
Species Habitat Index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_habitat_index.svg")
Relationships between valuation atlas and the species habitat index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_habitat_index_log.svg")
Log relationships between valuation atlas and the species habitat index
Species Protection Index
knitr::include_graphics("Outputs/Figures_Corpus_individual/species_protection_index.svg")
Relationships between valuation atlas and the species protection index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/species_protection_index_log.svg")
Log relationships between valuation atlas and the species protection index
Species Status Information Index
knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_status.svg")
Relationships between valuation atlas and the species status information index
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Species_status_log.svg")
Log relationships between valuation atlas and the species status information index
Total Wood Removals (roundwood, m3)
knitr::include_graphics("Outputs/Figures_Corpus_individual/Total_wood_removals.svg")
Relationships between valuation atlas and total wood removals
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Total_wood_removals_log.svg")
Log relationships between valuation atlas and total wood removals
Trends in forest extent (tree cover)
knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_forest_extent.svg")
Relationships between valuation atlas and trends in forest extent
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_forest_extent_log.svg")
Log relationships between valuation atlas and trends in forest extent
Nitrogen Deposition Trends
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_deposition_trends.svg")
Relationships between valuation atlas and nitrogen deposition trends
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_deposition_trends_log.svg")
Log relationships between valuation atlas and nitrogen deposition trends
Nitrogen Deposition Trends (kg N/ha/yr)
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_deposition_trends.svg")Relationships between valuation atlas and nitrogen deposition trends
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Nitrogen_deposition_trends_log.svg")Log relationships between valuation atlas and nitrogen deposition trends
Trends in Pesticides Use
knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_pesticides_use.svg")
Relationships between valuation atlas and trends in pesticides use
Log relationship graphs:
knitr::include_graphics("Outputs/Figures_Corpus_individual/Trends_in_pesticides_use_log.svg")
Log relationships between valuation atlas and trends in pesticides use